MULTIVARIATE METHODS FOR MONITORING STRUCTURAL CHANGE |
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| Authors: | Jan J J Groen George Kapetanios Simon Price |
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| Institution: | 1. Federal Reserve Bank of New York;2. School of Economics and Finance, Queen Mary, University of London, , UK;3. Bank of England and City University, , London, UK |
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| Abstract: | Detection of structural change is a critical empirical activity, but continuous ‘monitoring’ for changes in real time raises well‐known econometric issues that have been explored in a single series context. If multiple series co‐break then it is possible that simultaneous examination of a set of series helps identify changes with higher probability or more rapidly than when series are examined on a case‐by‐case basis. Some asymptotic theory is developed for maximum and average CUSUM detection tests. Monte Carlo experiments suggest that these both provide an improvement in detection relative to a univariate detector over a wide range of experimental parameters, given a sufficiently large number of co‐breaking series. This is robust to a cross‐sectional correlation in the errors (a factor structure) and heterogeneity in the break dates. We apply the test to a panel of UK price indices. Copyright © 2011 John Wiley & Sons, Ltd. |
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